When Data Entry Meant Endless Typing - Now It’s Done in Seconds
Not terribly long ago (think 10 years?), data entry was a tedious task. The world was steadily digitizing and was stuck in this in-between state. So a lot of documentation work had to be done twice, first in print form, then it had to be converted to a digital form.
In fact, this is still the case today; things like receipts, invoices, contracts, physical forms, etc, still have physical copies that need to be digitized. This used to be done by data entry operators, who spent hours typing numbers, names, and notes into spreadsheets or databases.
Today, that process takes seconds due to automation and AI. Today, we are going to explore how data entry has become trivialized due to technology.
How Data Entry Used to Be Done
In the early days of office digitization, data entry used to be done by clerks who would clack away at their keyboards all day. The work was boring to say the least, it was slow, and errors were far more common than they are now.
This caused all sorts of problems because typos could disrupt the integrity of digital records, and leadership could make wrong decisions because of it.
The work itself was extremely exhausting and boring, and that is a terrible combo because it leads to more mistakes. Thankfully, those days are now behind us, as there are many free solutions that make data entry fast, efficient, and accurate.
How Is Data Entry Done Now
Automation has come a long way since the early days of data entry. Nowadays, many repetitive tasks can be easily automated. Data entry in particular benefited from the widespread availability of OCR tools. OCR stands for optical character recognition.
Nowadays, we have OCR tools that recognize and extract text from an image and convert it to a machine-readable format. So, what data entry operators do is just scan the documents they need to digitize and input their pictures to the image to text converter, and voila, they have a digital version that they can simply copy and paste into the records.
This process of extracting text from images takes just a few seconds and is highly accurate, so there are almost no errors, and data integrity remains unharmed.
Other Technologies Used In Automatic Data Entry
Aside from OCR, there are other technologies at work, too. One of them is called NLP or natural language processing. If OCR lets computers understand that certain pixel arrangements (in images) are actually letters, then NLP allows them to make sense of these letters.
Meaning, they understand words, their semantic meaning, whether there are spelling mistakes in them, and whether the sentences they form are meaningful or not.
Image processing is another technology used before OCR. It is used to ‘prep’ the image so that the OCR engine has to do less work. This can include artifact removal, increasing the sharpness of characters, and making them stand out against the background with ease.
In modern image-to-text converters, all of these processes happen one after the other. They occur so fast that you still get your results in just a few seconds.
Everyday Uses of Automatic Data Entry That Save Hours
We have discussed automatic data entry and its myriad benefits, and even what technologies are used to make it work. But we didn’t discuss any practical uses, i.e., where it is most effective. After all, data entry is such a broad term that it could refer to any situation in which data needs to be manually entered into a digital database or document.
So, let’s check them out.
Business invoices and receipts.
Perhaps the most commonly digitized documents in the world are business invoices and receipts. Even if you get them in a digital form like PDF, you still need to ‘enter’ them into your company records somewhat manually.
Instead of typing totals or supplier names, you can scan and convert entire pages into spreadsheets or text files.
Academic research.
A lot of data entry is actually done by students and researchers. These people often have to extract quotes or references from scanned papers or pictures of their notes to add into their assignments or whitepapers. So, automated methods of data entry help them a lot by reducing the manual effort. As such, they can compile digital notes with ease.
Legal and medical documents.
The legal and medical industry is quite document-heavy. There are so many contracts, records, prescriptions, and medical documents that exist as physical documents, but need to be quickly digitized for record-keeping and recall.
So, quick digitization of handwritten notes or records reduces administrative overhead in these fields.
Personal organization
Nobody said that data entry cannot be for personal processes. In fact, many of us do data entry tasks somewhat regularly in our daily lives. Whether it's grocery lists, records of utility bills, taxes, or loan payments, we all keep some type of record to manage these things.
So, OCR tools help you store these personal records in a searchable, digital form.
Conclusion
Data entry used to be a tedious and error-prone process. It took a lot of time, couldn’t be handwaved away, but was utterly boring and spirit-declining.
Now, with OC, NLP, and image processing technologies all bundled up in one neat image-to-text converter, we can do this menial job with a lot more convenience. What took hours now takes minutes and leaves you free to do other important tasks.
So, make use of these tools and supercharge your data entry operations today.
